The AI revolution is upon us, and it’s important to be prepared for the changes it will bring. Artificial intelligence (AI) is set to disrupt industries and change the way we live and work. From healthcare to finance, transportation, and beyond, AI is poised to revolutionize the way we do things. To stay ahead of the curve and be ready for the changes that are coming, it’s important to understand the basics of AI and machine learning, develop skills in data science and analysis, learn to code, stay current on industry developments, and embrace change and new possibilities.

Educate yourself on the basics of AI and machine learning

To stay ahead of the curve, it’s important to understand the basics of AI and machine learning. AI is a broad term that encompasses a wide range of technologies, including machine learning, natural language processing, computer vision, and more. Machine learning, in particular, is a subset of AI that involves training computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Understanding the technology and its capabilities will help you make informed decisions about how to use it in your industry. There are plenty of online resources and courses available to help you learn more about AI and machine learning.

Develop skills in data science and analysis

As AI relies heavily on data, having a strong understanding of how to collect, organize, and analyze data will be crucial in the coming years. Data science is a field that involves using data to extract insights and make predictions. It’s a multidisciplinary field that combines statistics, computer science, and domain expertise to analyze and make sense of data. By developing skills in data science and analysis, you’ll be able to take advantage of the wealth of data that’s available and use it to make better decisions.

Learn to code

Even if you don’t plan on becoming a programmer, understanding how AI is created and implemented will be valuable in any industry. Coding is the language of AI, and understanding how to write code will give you a better understanding of how AI works and how it can be used in your industry. There are many resources available to help you learn how to code, from online tutorials to coding boot camps.

Stay current on industry developments

The AI field is constantly evolving, so it’s important to stay up to date on new technologies and advancements. By staying current on industry developments, you’ll be able to identify new opportunities and stay ahead of the curve. One way to do this is to attend AI conferences and events, which provide a great opportunity to learn about new technologies and network with other professionals in the field.

Embrace change and be open to new possibilities

The AI revolution will bring both challenges and opportunities, so being open to new ways of thinking and working will be key to success. Embracing change and being open to new possibilities will allow you to take advantage of the opportunities that AI presents. It’s important to be proactive and be prepared for the changes that are yet to come.

Conclusion

AI has the potential to change the way we live and work, and it’s important to be prepared for the changes it will bring. By educating yourself on the basics of AI and machine learning, developing skills in data science and analysis, learning to code, staying current on industry developments, and embracing change and new possibilities, you’ll be well-positioned for success in the age of AI. Remember, AI comes not only with disruptions but also with many new opportunities and possibilities. It’s up to us to be proactive and ready for the future.

In conclusion, the AI revolution is here, and it’s important to be prepared for the changes it will bring. By understanding the basics of AI and machine learning

Thanks for reading!

Note -“We cannot direct the wind, but we can adjust the sails.”

Don’t forget to give me your 👏 !

How To Make Sure You Don’t Lose Your Job To Artificial Intelligence! was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Day 6: Advance SQL For Data Science

This blog contains type of joins like Inner join, Left join, Right join , Full join, Self join and Cross join.

A JOIN clause is used to combine rows from two or more tables, based on a related column between them.

1. Self Join

A self-join is a regular join, in which the table joins itself.

Syntax of self-join:

SELECT column_name(s) from table1 T1, table1 T2
WHERE condition;

Query: select T1.user_id , T1.name, T2.user_id, T2.name from user T1, user T2;

NOTE: T1 and T2 are different table aliases for the same table.

2. Inner Join

The INNER JOIN keyword selects records that have matching values in both tables.

Syntax of Inner join :

SELECT column_name(s) FROM table1
INNER JOIN table2 ON table1.column_name = table2.column_name;

Query: select * from user u
inner join Guest G on G.Guest_user_id = u.user_id;

Inner join provides only rows in which data is matched on both sides of the table.

3. Left Join

The LEFT JOIN keyword returns all records from the left table, and the matched records from the right table.

Syntax of Left-join:

SELECT column_name(s) FROM table1
LEFT JOIN table2 ON table1.column_name = table2.column_name;

Query: select * from user u left join Guest G on G.Guest_user_id = u.user_id;

In Left join, we are applying left join to the user table which is mentioned first (In Query). It will return all the rows from table 1 i.e. user table and only matched rows from table 2 i.e. Guest table.

4. Right Join

The RIGHT JOIN keyword returns all records from the right table and the matched records from the left table.

Syntax of Right-join:

SELECT column_name(s) FROM table1
RIGHT JOIN table2 ON table1.column_name = table2.column_name;

Query: select * from user u
right join Guest G on G.Guest_user_id = u.user_id;

In right join, it returns all the rows from the right table i.e. Guest table and only matched rows from the first table i.e. user table.

5. Full Join

The Full JOIN keyword returns all records when there is a match in either the left or the right table.

Syntax of full join:

SELECT column_name(s) FROM table1
FULL OUTER JOIN table2 ON table1.column_name = table2.column_name;

6. Cross Join

The CROSS JOIN keyword returns all records from both tables (table1 and table2).

Syntax of Cross-join:

SELECT column_name(s) FROM table1
CROSS JOIN table2;

Query: select * from user u cross join Guest G

Note: On clause is optional in Self and cross join.

JOINS with Aggregate/Ranking Functions

We can use the Aggregate, ranking and etc functions with joins and can manipulate queries as we want.

Here is a short example of left join with group by and having clause.

Query: select user_id, u.name, min(u.age) from user u
left join Guest G on G.Guest_user_id = u.user_id
Group by user_id, u.name having min(G.age) <= 30;

Thank you for reading it and liking it.

=============================THE END==========================

GitHub: Day 5 Session

Please give it a star on Git Hub!!

Reference :

1. https://www.w3schools.com/sql/default.asp
2. https://www.geeksforgeeks.org/

Hope you found it helpful! Thanks for reading!

Follow me for more Data Science related posts!

Let’s connect on LinkedIn!

Day 5: Advance SQL For Data Science was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Day 5: Advance SQL For Data Science

This blog contains Window Ranking function in SQL like (Rank, Dense_Rank, Row_Number , Lead, Lag) .

Rank()

This RANK() function calculates a rank to each row within a partition of a result set.

The Syntax of Rank() window function :-

RANK() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

• In PARTITION BY clause, it divides the rows of the result set partitions to which the rank() function is applied.
• In ORDER BY The clause specifies whether the row needs to be sorted into asc/dec order of the rows for each partition to which the Rank() function is applied.

Here is the Example of Rank()

We are applying Rank() on Salary Column: select *, rank() over (order by salary desc) rn from Employee e;

The rank() function splits the salary column on the bases of descending order.

Now we are applying Rank() with Partition by select *, rank() over (Partition by name order by salary desc) rn from Employee e

In this Rank() function splits on the partition of the name column with the salary column in descending order.

DENSE_RANK()

The DENSE_RANK() function returns consecutive rank values with each row in each partition receiving the same ranks if they have the same values.

The Syntax of Dense_Rank() window function :-

DENSE_RANK() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

We are applying Dense_Rank() on Salary Column: select *, dense_rank() over (order by salary desc) rn from Employee e;

Now we are applying Dense_Rank() with Partition by: select *, dense_rank() over (Partition by name order by salary desc) rn from Employee e

In this Dense_Rank() function splits on the partition of the name column with the salary column in descending order.

ROW_NUMBER()

The ROW_NUMBER() is a simple window function that gives an integer row number to the corresponding row. The row number starts with 1 for the first row in each partition.

The Syntax of Row_number() window function :-

ROW_NUMBER() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

We are applying row_number() on Salary Column: select *, rank() over (order by salary desc) rn from Employee e;

we are applying row_number() with Partition by: select *, rank() over (Partition by name order by salary desc) rn from Employee e;

In this Row_Number() function splits on the partition of the name column with the salary column in descending order.

LEAD() and LAG()

LAG() and LEAD() are positional functions. These are window functions and are very useful in creating reports because they can refer to data from rows above or below the current row

The LAG() function allows access to a value stored in a different row above the current row.

Syntax of LAG():

LAG(expression [,offset[,default_value]]) OVER(ORDER BY columns)

Query: SELECT *, LAG(Salary) OVER(Partition By name ORDER BY Salary asc) as previous_sale_value FROM Employee;

LEAD() is similar to LAG(). Whereas LAG() accesses a value stored in a row above, LEAD() accesses a value stored in a row below.

Syntax of Lead():

LEAD(expression [,offset[,default_value]]) OVER(ORDER BY columns)

Query: SELECT *, LEAD(Salary) OVER(Partition By name ORDER BY Salary asc) as previous_sale_value FROM Employee;

NOTE: The PARITION BY clause is optional. If you omit it, the function will treat the whole result set as a single partition.

Thankyou for reading it.

=============================THE END==========================

GitHub : Day 4 Session

Please give it a star on github!!

Reference :

1. https://www.w3schools.com/sql/default.asp
2. https://www.geeksforgeeks.org/

Hope you found it helpful! Thanks for reading!

Follow me for more Data Science related posts!

Let’s connect on LinkedIn!

Day 4: Advance SQL For Data Science was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Human resource management (HRM) is a critical aspect of any organization as it involves managing the workforce and ensuring that their needs are met. However, HRM faces several challenges that can hinder the performance of the organization. In this article, we will discuss some of the challenges faced by HRM and the role of artificial intelligence (AI) in addressing these challenges in 2023.

Challenges Faced by Human Resource Management

1. Attracting and Retaining Talent

One of the biggest challenges faced by HRM is attracting and retaining top talent. Organizations struggle to find and retain the best employees in a highly competitive job market. This is especially true for high-demand roles such as data scientists, software engineers, and digital marketing specialists.

2. Managing Employee Diversity and Inclusion

Another significant challenge faced by HRM is managing employee diversity and inclusion. With the increasing diversity of the workforce, organizations must ensure that all employees are treated fairly and with respect. This includes creating a culture of inclusion, providing training and education, and addressing discrimination and bias.

3. Managing Employee Engagement

HRM also faces the challenge of managing employee engagement. With the rise of remote work and flexible schedules, keeping employees engaged and motivated can be difficult. This can lead to decreased productivity and higher turnover rates.

4. Managing Employee Data

HRM also faces the challenge of managing employee data. With the increasing use of technology, organizations must ensure that employee data is accurate, up-to-date, and secure. This includes managing employee information, performance data, and compliance with data privacy laws.

The Role of Artificial Intelligence in Addressing HRM Challenges

1. Attracting and Retaining Talent

AI can help HRM attract and retain talent by automating the recruitment process. This includes using AI-powered chatbots to answer candidate questions, using machine learning algorithms to analyze resumes and identify the best candidates, and using predictive analytics to identify high-potential employees.

2. Managing Employee Diversity and Inclusion

AI can also help HRM manage employee diversity and inclusion by automating the performance review process. This includes using machine learning algorithms to identify bias and discrimination in performance evaluations, providing training and education to employees on diversity and inclusion, and addressing discrimination and bias.

3. Managing Employee Engagement

AI can help HRM manage employee engagement by automating the employee engagement survey process. This includes using machine learning algorithms to identify areas of improvement, providing feedback and coaching to employees, and tracking progress over time.

4. Managing Employee Data

AI can help HRM manage employee data by automating the data management process. This includes using machine learning algorithms to identify errors and inconsistencies, providing real-time updates, and ensuring compliance with data privacy laws.

Conclusion

HRM faces several challenges that can hinder the performance of the organization. However, AI can help address these challenges by automating recruitment, performance evaluations, engagement surveys, and data management. As we move into 2023, we can expect to see more organizations leveraging AI to improve their HRM processes.

Human Resource Management Challenges and The Role of Artificial Intelligence in 2023 was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data Scientists : The Business Transcribers of the Cyber verse

Who are we?

We are scientists that study the most important resource of the world today which is known as “Data".

Our job is to understand its origin, context, and application to a relevant scenario.

What are our objectives?

Every dayyou hear a very common statement in your workplace “Hey Dude send me the data”.

When people ask for data to get their work done and measure the performance of their work. They often don’t know the workings and efforts gone into making it and also how to advance it further to keep up with new trends in their work and industrial skillset.

Data Scientists are both responsible for assisting industrial transformation and also being the catalysts for it.

Known work?

Netflix

Spotify

Netflix’s and Spotify’s recommendation engine is one of the record-breaking works of Data Scientists in partnership with Developers where the recommendations for movies and songs exponentially increased subscribers and made both the firms Industry 4.0 Rockstars.

Google Translate

Using Natural language processing algorithms for programming. Data scientists compiled a very complex and strongly tuned dataset with various languages and terms and are able to respond to audio recordings with textual readings, which has reduced many global communication gaps today and is used by 500 million people globally.

What is our future?

We are individuals who will study data and transform superstructures with it.

Every institution and organization needs to act on the analysis of its data activity and how design strategies based on them. We as Data Scientists would advise them and assist them with the implementation of the following relatable scenarios:

• R&D of new products and Services with the partnership of manufacturing experts and domain specialists.
• Designing Business strategies and policies with the partnership of Business Development experts.
• Improve existing pipelines and procedures of business activities and data transfers with the partnership of operations experts.
• Using new tools for business conduct and workplace practices with the partnership of human resource experts.

Authors Note

This is based on my knowledge, research, and experience in the field to date and it’s always subject to disagreement and dissolution.

I look forward to learning from the feedback given by my readers.

Who am I?

Hey Guys I’m currently Studying for my MSc in Data Science from the university of London and working as a freelancer in Analytics on Upwork. If you have any reviews, critics, or any need of advice for any analytics/Data Science/Machine Learning based project. Feel free to reach out to me on LinkedIn and you may use my Github/Kaggle repository of python and R code templates and already-made visualization’s for implementation or reference.

LinkedIn: https://www.linkedin.com/in/goto-resumemuhammad-ammar-jamshed-029280145/

GitHub: https://github.com/AmmarJamshed

Kaggle: https://www.kaggle.com/muhammadammarjamshed

Data Scientists : The Business Transcribers of the Cyber verse was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data Science

You heard this term most of the time all over the internet, as well this is the most concerning topic for newbies who want to enter the world of data but don’t know the actual meaning of it.

And for searching the term you landed on multiple blogs, articles as well YouTube videos, because this is a very vast topic, or I, would say a vast Industry.

I’m not saying those are incorrect or wrong even though every article has its mindset behind the term ‘Data Science’.

And in today’s article, I’m sharing my perspective on the term ‘Data Science’, and whatever I have learned till now.

What is Data Science?- a beginner question

Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis”, is the definition enough explanation of data science? Well, we can say this is a kind of explanation but data science is more than that.

In my view “It’s a science of getting and mining the insights from the data and those insights will help businesses to grow. And it’s not a technology, it’s a process”.

Just like other processes it also has some tools and technology to make the whole process fruitful, and “It’s not just a Model Building”

Why am I saying this? Let’s understand with an example if we consider web development so there are UI, UX, Database, Networking, and Servers and for implementing all these things we have different-different tools-technologies and frameworks, and when we have done with these things we just called this process as web development.

Just like this in Data Science we have Data Analysis, Business Intelligence, Databases, Machine Learning, Deep Learning, Computer Vision, NLP Models, Data Architecture, Cloud & many things, and the combination of these technologies is called Data Science.

Data Science and AI are related?

After understanding data science let’s discuss the second concern “Data Science vs AI”. So, we know that data science is a process of getting insights from data and helps the business but where this Artificial Intelligence(AI) lies?

First understand ML and DL so, in Machine learning and Deep learning we perform some mathematical operations on data and make the models, and these models help us to predict future outcomes.

So, it looks like magic but it’s not magic. There is mathematics behind those models and predictions.

If we talk about AI. So, a system that will mimic humans is known as an AI system, and if we look at the ML and DL so these technologies are also doing the same thing, those ML or DL models are capable of predicting future outcomes.

In simple words AI is not a technology, it’s terminology. AI is something where the different-different technology stacks are implemented and built into a system that will be able to make some human intelligence.

So, AI does not lie in the Data Science or outside of Data science, instead, the product of data science refers to an AI solution, but maybe in near future, there will be some technology that will directly work on AI such as humanoid and smart systems without human supervision.

Is Data science changing the world?

Instead of saying that Data science is changing the world, we can say Data science is helping the world to grow by using data.

Data science will help the business to identify their problems and loopholes as well as give the solution to those problems.

So, data science provides the solution to a business from all directions, and nowadays most businesses use Data Science irrespective of whether the business is small-scale or large-scale business.

If a business produces the data (and we know all businesses produce data) so data science is the right process to crunch that data and get some useful insights from that as per the business use case and problem statement.

Or in other words, we can say that Data Science is the only process that will correctly use the data.

And that’s the reason there is increasing job demand in the Data Science domain.

How data science helps the businesses

We know that data science helps to extract insights from data, so how this insight will help the business for understanding this question so, first we have to understand a business use case. (This is a random use case, just for understanding the question)

“ Let’s suppose you have a product-based business where you manufacture and sell the products and there are some products you sold all over the globe like product A, product B, and product C.

Since you have the manufacturing as well as selling units, these units generate some data regarding their work.

There is a Data Science team that is working on the data of both units, and they come up with insights like your Product A is highly demanded in Asia region as compared to the US region whereas your Product B is highly demanded in the US region as compared to Africa region and the Product C is highly demanded in Africa region as compare to US and Asia.

Now you have the insights into the products and for generating more profit you can make a decision like you will increase the manufacturing unit of Product A in Asia or just do targeted marketing of this product only in the Asia region,

and on the other side you will find a problem like why Product B and Product C is not making a profit from Asia Region? and this thing also you will find with help of data science.

Remainder: This is just a random use case as well as random insights.

You will do the same thing with all the products, now you can see how data science is useful for making the business more profitable.

Or in other words, we can say with the help of data science you can identify as well as approach the problem.”

Nowadays most businesses use data science, whether a business is product-based or service-based they use data science for their growth.

Data Science and Big Data

There is an Umbrella of Big data and what is Big Data? so, as per its name, Big Data consists enormous amount of data which will be defined by the 4’V Volume, Velocity, Veracity, and Variety.

Where Volume means the amount of Data, Velocity means how frequently data is generated? or we can say the speed of generating the data, and Veracity means how truthful the data is. Variety means the format of data such as text, image, audio, and video.

We produce 2.5 quintillion bytes of data every day, you can get a rough estimate of how much data is generated till now and how much data will be in the future.

And for handling this much amount of data we use such technologies as Big Data and Data Science.

Now, Big Data technologies mostly focus on things like Data Mining, Data Warehousing, Preprocessing Data, and Storing the Data, and Data Science technologies are more towards the Analytical part.

Big Data has the ETL(pipelining), Data engineering, Hadoop, Data Warehousing, and Data Mining whereas Data Science has Mathematics, Machine learning, Deep Learning, Computer Vision, NLP, RL, AIOps, Data Reporting, Dashboarding, and all.

In simple words, Big Data is for handling and managing an enormous amount of data and Data science is for applying mathematics to data so that we can get some insights from it.

Final Words

Many of my friends ask me the question about “Data Science”, so I thought that through this article I will be able to explain ‘Data Science’ to all those people who do not know anything about Data Science.

So you understood Data Science with the help of this article and I hope that I was able to explain to you the correct definition of Data Science. Whatever I have shared in this article is my experience so far, I am not an expert but a student of data science, I hope you learned something from this article.

A beginner tale of Data Science was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction:

Learning is something that we have to do every day. As a developer, you need to learn the latest and hottest technologies because if you don’t, you might not be able to succeed in this field.

I am a web developer and machine learning engineer. I am working on both and trying to improve my skills with time, but sometimes you need guidance to see where you are right now and where you see yourself in the future.

So for this blog, I am going to give you a perfect road map through which you can learn the basics and then start training your own different models for machine learning.

Road Map:

In order to be good at machine learning or deep learning, you must know programming, how to code, how to solve problems, and what kind of logic you will use to solve a complex problem.

1. Learning a Programming Language:

The very first thing you must do is learn a programming language, and for that, I will recommend learning Python.

Learn the basics of the Python programming language, Get comfortable with the Python programming language, try to build your logic, and Learn how to solve a problem; the more you code, the better you will get.

Python is great for everything; you can do anything with the Python programming language: build websites, develop games, train machines to predict anything, or train machines to generate anything, like text and images. There is so much you can do with Python.

Learn Python libraries:

Once you’ve got all the basics down for machine learning, you need to learn some basic libraries so that you can get the job done, and for that, I would recommend a couple of libraries:

Numpy

NumPy is a popular Python library for multi-dimensional array and matrix processing because it can be used to perform a great variety of mathematical operations. Its capability to handle linear algebra, the Fourier transform, and more makes NumPy ideal for machine learning and artificial intelligence (AI) projects, allowing users to manipulate the matrix to easily improve machine learning performance. NumPy is faster and easier to use than most other Python libraries.

Scipy

With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson 2001 decided to merge most of these bits and pieces of codes and standardize them. The resulting library was then named as SciPy library.

The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.

The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.

Scikit-learn

Scikit-learn is a very popular machine-learning library that is built on NumPy and SciPy. It supports most of the classic supervised and unsupervised learning algorithms, and it can also be used for data mining, modeling, and analysis. Scikit-learn’s simple design offers a user-friendly library for those new to machine learning.

Pandas

Pandas is another Python library that is built on top of NumPy, responsible for preparing high-level data sets for machine learning and training. It relies on two types of data structures, one-dimensional (series) and two-dimensional (Data frame). This allows Pandas to be applicable in a variety of industries including finance, engineering, and statistics. Unlike the slow-moving animals themselves, the Pandas library is quick, compliant, and flexible.

Matplotlib

Matplotlib is a Python library focused on data visualization and primarily used for creating beautiful graphs, plots, histograms, and bar charts. It is compatible with plotting data from SciPy, NumPy, and Pandas. If you have experience using other types of graphing tools, Matplotlib might be the most intuitive choice for you.

These libraries are great for reading data and then storing it in multiple arrays, variables, or data frames. Once you have stored your data, you then need to display that data in the form of different graphs. I have provided all of the official documentation. So review them, and they can help you out.
One more thing: read the official documentation; that will help you like crazy.

2. Learn Maths:

Here we go. Whenever I mention maths, people say you don’t need maths for machine learning, but believe me, you need to learn maths, and moreover, you need to learn these subjects to be a full-fledged god in machine learning and deep learning.

• Probability and Statistics
• Linear Algebra
• Calculus
• Matrix

Each subject has a purpose. Probability and statistics will assist you in reading various types of data so that you can determine how data works and what the range of each dataset is. Linear algebra and matrices will help you reshape the data according to the model. Once you know how to play and reshape the data, you can do anything with the given data, no matter how large or impure the dataset is. So Learn them they will help you out.

3. Learn different ML Models:

Once you have learned how to code in Python and how math works in machine learning, you need to learn different machine-learning models. There are so many out there, and each model has a purpose. Study them and find out what makes them unique. How each model is working, what are the inputs, what will be the output, what kind of data is required, and how can you reshape the data for that model?

There is a great place where you can find different machine learning models, and that place is called Kaggle.
Kaggle is great for learning about machine learning models; you can find a lot of datasets and even check out different machine learning models.

You can also get a lot of help from Github. GitHub is great for every developer because you can save all of your versions in one place.

4. Create your Own Model:

Once you know how to read a model and code a model, now you need to create your own model, using different python libraries some of which are stated below:

TensorFlow

TensorFlow’s open-source Python library specializes in what’s called differentiable programming, meaning it can automatically compute a function’s derivatives within a high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework. TensorFlow can be used to visualize machine learning models on both desktop and mobile.

Seaborn

Seaborn is another open-source Python library, one that is based on Matplotlib (which focuses on plotting and data visualization) but features Pandas’ data structures. Seaborn is often used in ML projects because it can generate plots of learning data. Of all the Python libraries, it produces the most aesthetically pleasing graphs and plots, making it an effective choice if you’ll also use it for marketing and data analysis.

Theano

Theano is a Python library that focuses on numerical computation and is specifically made for machine learning. It is able to optimize and evaluate mathematical models and matrix calculations that use multi-dimensional arrays to create ML models. Theano is almost exclusively used by machine learning and deep learning developers or programmers.

Keras

Keras is a Python library that is designed specifically for developing neural networks for ML models. It can run on top of Theano and TensorFlow to train neural networks. Keras is flexible, portable, user-friendly, and easily integrated with multiple functions.

PyTorch

PyTorch is an open-source machine-learning Python library based on the C programming language framework, Torch. It is mainly used in ML applications that involve natural language processing or computer vision. PyTorch is known for being exceptionally fast at executing large, dense data sets and graphs.

You need to learn these libraries; once you have, you can create your model. You will learn how to train a model, how to test a model, and what ratios you can use to create a good machine learning or deep learning model.

5. Build your Project & Save them:

Learn how to use git and GitHub; this is a critical skill for any developer. This will help you save your project and show the world that you have done some projects and have the skills to take care of problems.

The second most important thing to learn is how to document your code and your project, because you will forget everything after a while, and without documentation, it will take forever to find the solution to a problem that you have already solved in the past.

Project Ideas:

Now all you need are some project ideas so that you can practice. So here are some of the project ideas that I have used in the past.

• Use machine learning to forecast your own earnings.
• Forecast your spending for the next few months.
• Do some machine learning challenges on Kaggle.
• Do image processing and image restoration using deep learning and machine learning.

Conclusion:

The current market is thought and without skills, you can’t survive so learn coming technologies and be great. Learn what you like. Don’t listen to others.

Here is the roadmap that I used to learn machine learning, and I hope you will like the blog. I will see you next time.

Contact Info:

Name: Abdul Rafay

Email: 99marafay@gmail.com

website: https://rafay99.info/

Blog Website:

1. Medium
2. Personal Website

Please feel free to contact me if you have any questions.

Road Map to Machine Learning & Deep Learning was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

According to the National Sleep Foundation, it is estimated that 50–70 million adults in the United States have a sleep disorder.

Sleep disorders can have serious implications on a person’s physical and mental health. Some of the potential effects include:

• Increased risk of accidents and injuries, due to drowsy driving, falling asleep at the wheel, or other lapses in attention
• Chronic fatigue and exhaustion, which can lead to decreased productivity and overall quality of life
• Increased risk of developing chronic health conditions, such as obesity, diabetes, high blood pressure, and heart disease
• Increased risk of depression and anxiety
• Increased risk of stroke and cardiovascular disease -Impact on cognitive function, memory, and ability to learn.

Diagnosing sleep disorders can be challenging for several reasons:

• Many people with sleep disorders are not aware they have one, and may not seek medical attention until the disorder has caused significant problems in their life.
• Sleep disorders can be caused by a wide range of factors, including medical conditions, medications, lifestyle habits, and psychological issues. This makes it difficult to pinpoint a specific cause.
• Some sleep disorders, such as sleep apnea and insomnia, can be difficult to diagnose because they do not always produce obvious symptoms.
• Many sleep disorders are not visible during normal daytime activities, and require specialized testing, such as polysomnography, in a sleep lab to be diagnosed.

Sleep medicine is a subspecialty of several medical fields, including pulmonology, neurology, psychiatry, and otolaryngology. Physicians who specialize in sleep medicine have completed additional training and education in the diagnosis and treatment of sleep disorders.

Access to sleep specialists and sleep disorder treatment can be limited in underserved communities. Factors such as lack of insurance coverage, limited availability of healthcare providers in certain areas, and transportation challenges can make it difficult for people in these communities to access the care they need. Additionally, cultural, linguistic, and economic barriers can also play a role in preventing individuals from seeking and receiving care. According to the American Academy of Sleep Medicine, underserved populations, including racial and ethnic minorities, low-income individuals, and rural residents are disproportionately affected by sleep disorders and have less access to appropriate care.

When patients are not diagnosed timely with their sleep disorders, several negative effects can occur:

• Worsening of symptoms: If a sleep disorder is not diagnosed and treated, symptoms may worsen over time. For example, if a patient has sleep apnea, their sleep disruptions and low oxygen levels can lead to high blood pressure, heart disease, or stroke.
• Reduced quality of life: If a sleep disorder is not diagnosed and treated, it can have a negative impact on a patient’s quality of life. Symptoms such as insomnia, daytime sleepiness, and fatigue can make it difficult for patients to function in their daily lives.
• Increased healthcare costs: If a sleep disorder is not diagnosed and treated, it can lead to more frequent and longer hospital stays, as well as additional medical procedures and treatments.
• Increased risk of accidents: If a sleep disorder is not diagnosed and treated, it can increase the risk of accidents, such as car accidents or falls.
• Other health problems: If a sleep disorder is not diagnosed and treated, it can lead to other health problems, such as depression, anxiety, and cognitive impairment.

Integrated care teams play an important role in treating patients with sleep disorders. An integrated care team is made up of healthcare professionals from different disciplines who work together to provide coordinated, patient-centered care. In the context of sleep disorders, an integrated care team may include:

• Sleep specialists: such as pulmonologists, neurologists, or otolaryngologists, have expertise in the diagnosis and treatment of sleep disorders.
• Primary care physicians: who can refer patients to specialists and provide ongoing care and monitoring of sleep disorders.
• Nurses: who can provide education and support to patients with sleep disorders.
• Psychologists or behavioral therapists: who can provide cognitive behavioral therapy (CBT) for insomnia, which is an evidence-based treatment for insomnia.
• Technologists: who can provide sleep studies and other diagnostic tests.
• Dietitians or nutritionists: who can provide education and recommendations on nutrition and lifestyle changes that can improve sleep.
• Pharmacists: who can provide education on medications used to treat sleep disorders, and monitor for potential drug interactions.

An integrated care team can help to ensure that patients with sleep disorders receive appropriate care and treatment. They can also help to improve patient outcomes by providing coordinated, patient-centered care that addresses the physical, emotional, and social aspects of sleep disorders.

The ideal care team composition to treat patients with sleep disorders will depend on the specific needs of the patient and the type of sleep disorder they have. However, a typical care team for sleep disorders may include the following members:

• Sleep specialist: A sleep specialist, such as a pulmonologist, neurologist, or otolaryngologist, who has expertise in the diagnosis and treatment of sleep disorders.
• Primary care physician: A primary care physician who can refer patients to specialists and provide ongoing care and monitoring of sleep disorders.
• Nurse: A nurse who can provide education and support to patients with sleep disorders.
• A psychologist or behavioral therapist: A psychologist or behavioral therapist who can provide cognitive behavioral therapy (CBT) for insomnia, which is an evidence-based treatment for insomnia.
• Technologist: A technologist who can provide sleep studies and other diagnostic tests.
• Dietitian or nutritionist: A dietitian or nutritionist who can provide education and recommendations on nutrition and lifestyle changes that can improve sleep.
• Pharmacist: A pharmacist who can provide education on medications used to treat sleep disorders, and monitor for potential drug interactions.

Examples of integrated care treatment plans for patients with sleep disorders include:

1. Behavioral therapy-based treatment plan: This plan may include cognitive behavioral therapy (CBT) for insomnia, which is an evidence-based treatment for insomnia. This therapy may be provided by a therapist or psychologist and may include techniques such as sleep restriction, stimulus control, and relaxation training.
2. Medication-based treatment plan: This plan may include the use of medication to treat specific sleep disorders, such as sleep apnea, insomnia, and restless legs syndrome. Medications may include sedative-hypnotics, melatonin receptor agonists, or other medications that target the specific sleep disorder.
3. Lifestyle changes-based treatment plan: This plan may include recommendations for changes in diet, exercise, and sleep hygiene, as well as the use of light therapy or other non-pharmacological approaches to improve sleep.
4. Medical device-based treatment plan: This plan may include the use of medical devices such as continuous positive airway pressure (CPAP) machines, actigraphy devices, or portable sleep monitoring devices.
5. Combination treatment plan: This plan may include a combination of different types of treatment, such as a combination of behavioral therapy, medication, and lifestyle changes, tailored to the specific needs of the patient.

PatientSphere by Open Health Network is a digital platform that can help with managing integrated care plans for patients with sleep disorders in several ways:

1. Providing a centralized location for patient information: PatientSphere can be used to store and share patient information, such as sleep study results, treatment plans, and patient progress, among the different members of the care team.
2. Improving communication and coordination: PatientSphere can be used to facilitate communication and coordination among the different members of the care team, such as sleep specialists, primary care physicians, and behavioral therapists.
3. Personalized treatment plans: PatientSphere can be used to create personalized treatment plans for patients with sleep disorders. This can include a combination of different types of treatment, such as a combination of behavioral therapy, medication, and lifestyle changes, tailored to the specific needs of the patient.
4. Monitoring and tracking patient progress: PatientSphere can be used to monitor and track patient progress over time. This can include tracking sleep patterns, medication usage, and other relevant information, which can be used to make adjustments to the treatment plan as needed.
5. Remote monitoring and telemedicine: PatientSphere can also facilitate remote monitoring and telemedicine, allowing patients to access care and treatment remotely, which can be beneficial for patients who have difficulty accessing in-person therapy, and provide a convenient and cost-effective way for patients to manage their sleep disorders.

There are several new innovations and areas of research related to sleep disorders have emerged in recent years. Some of these include:

• Wearable technology: Wearable devices such as smartwatches and fitness trackers can track sleep patterns, detect sleep apnea, and monitor other sleep-related data such as heart rate, movement, and oxygen levels.
• Digital therapeutics: Software-based interventions that are clinically validated and used to treat or manage medical conditions, such as insomnia.
• Artificial Intelligence and Machine Learning: AI and ML technologies are being used to analyze patient data, predict the risk of developing sleep disorders and make personalized treatment recommendations.
• Digital Twin: Virtual models of a physical system, process, or person, that can be used to simulate and analyze real-world conditions, such as sleep patterns.
• Medical Devices: there are new medical devices have been developed such as continuous positive airway pressure (CPAP) devices, Actigraphy, and portable sleep monitoring devices, which are used for the diagnosis and treatment of sleep disorders.
• Non-Pharmacological Approaches: Studies have been conducted to explore the effectiveness of non-pharmacological approaches such as mindfulness-based interventions, light therapy, and physical activity in treating sleep disorders.
• Research in sleep-related disorders: There has been a significant increase in research on sleep-related disorders such as narcolepsy, insomnia, sleep apnea, restless legs syndrome, and other sleep-related disorders to understand the underlying causes, and develop new treatments.

Digital health has the potential to play a significant role in helping patients with sleep disorders. Digital health technologies, such as mobile apps, remote monitoring devices, and digital therapeutics, can provide a more convenient, accessible, and cost-effective way for patients to manage their sleep disorders.

Some examples of digital health technologies that can be used to help patients with sleep disorders include:

• Mobile apps: There are a variety of mobile apps available that can help patients track their sleep patterns, set sleep goals, and receive personalized sleep recommendations. Some apps also incorporate features such as white noise generators, guided meditation, and sleep-promoting music to help with insomnia.
• Remote monitoring devices: Wearable devices such as smartwatches and fitness trackers can be used to track sleep patterns, detect sleep apnea, and monitor other sleep-related data such as heart rate, movement, and oxygen levels.
• Digital therapeutics: These are clinically validated software-based interventions that are used to treat or manage medical conditions. Digital therapeutics can be used to provide cognitive behavioral therapy for insomnia (CBT-I) or other evidence-based treatments for sleep disorders, remotely.
• Telemedicine: Telemedicine can be used to connect patients with sleep specialists for remote consultations, follow-up appointments, and monitoring of their sleep disorder treatment.

There are a variety of mobile apps available that can help patients with sleep disorders track their sleep patterns, set sleep goals, and receive personalized sleep recommendations. Some popular apps include Sleep Cycle, Sleep Time, and Sleep as Android, to name a few. These apps can be helpful in providing information on sleep patterns and helping users identify potential issues.

Some pros of using mobile apps for patients with sleep disorders include:

• Convenience: Mobile apps can be used at any time and from any location, making it easy for patients to track their sleep patterns and set sleep goals.
• Personalization: Many apps can provide personalized recommendations based on a patient’s sleep data, which can help to identify specific issues and target them more effectively.
• Cost-effective: Many sleep-tracking apps are free or low-cost, making them accessible to a wide range of patients.
• Motivation: Some apps include features such as progress tracking, which can help to motivate patients to make changes that can improve their sleep.

Some cons of using mobile apps for patients with sleep disorders include:

• Data accuracy: Apps rely on self-reported data, which may not be entirely accurate.
• Lack of proper diagnosis or treatment plan: Many apps do not provide a proper diagnosis or treatment plan for sleep disorders, and it is recommended to consult with a healthcare provider for a proper diagnosis and treatment.
• Lack of clinically validated data: Some apps may not be clinically validated, meaning that the information provided may not be scientifically accurate or evidence-based.
• Consistency: Some users may have trouble using the apps consistently, which can make it difficult to track patterns or see improvement

Digital therapeutics (DTx) are clinically validated software-based interventions that are used to treat or manage medical conditions. Some digital therapeutics that are available for patients with sleep disorders include:

• CBT-I Coach: This app provides cognitive behavioral therapy for insomnia (CBT-I), which is an evidence-based treatment for insomnia. It includes modules on sleep education, sleep diaries, relaxation techniques, and goal setting.
• Sleepio: This app provides a sleep improvement program that is based on cognitive behavioral therapy for insomnia (CBT-I). It includes modules on sleep education, sleep diaries, and personalized sleep recommendations.
• SHUTi: This app provides cognitive behavioral therapy for insomnia (CBT-I) and includes modules on sleep education, sleep diaries, and personalized sleep recommendations.
• DreamMoods: This app provides sleep education, a sleep diary, and personalized recommendations based on dream analysis.
• Re-timer: This app provides light therapy, which is a treatment for insomnia and other sleep disorders.

Digital therapeutics (DTx) can help improve the health of patients with sleep disorders in several ways:

• They can provide access to evidence-based treatments: Digital therapeutics such as CBT-I Coach, Sleepio, and SHUTi provide cognitive behavioral therapy for insomnia (CBT-I), which is an evidence-based treatment for insomnia. This can help patients improve their sleep patterns, increase the time they spend in bed asleep, and reduce insomnia symptoms.
• They can provide personalized recommendations: Digital therapeutics can provide personalized recommendations based on the patient’s sleep patterns and other data. This can help patients identify specific issues and target them more effectively.
• They can improve adherence to treatment: Digital therapeutics can e used in the convenience of the patient’s home and can be used at any time, which can improve adherence to treatment. This can help to ensure that patients receive the appropriate amount of treatment and that treatment is more effective.
• They can reduce healthcare costs: Digital therapeutics can be a more cost-effective alternative to in-person therapy, which can reduce healthcare costs for patients.
• They can improve patient engagement: Digital therapeutics can help to increase patient engagement by providing a more interactive and personalized experience. This can help to ensure that patients are more involved in their own care and are more motivated to make changes that can improve their sleep.

Digital therapeutics for patients with sleep disorders have several potential cons:

1. Limited effectiveness: Some digital therapeutics may not be as effective as traditional therapies, such as cognitive behavioral therapy (CBT) or medication.
2. Lack of regulation: There is currently a lack of regulation for digital therapeutics, which means that there may be variations in the quality and safety of different products.
3. Limited access: Digital therapeutics may not be accessible to all patients, particularly those in underserved communities who may not have access to smartphones or the internet.
4. Limited human interaction: Some patients may prefer face-to-face therapy rather than digital therapy, as it can provide them with more personalization, emotional support, and a sense of accountability.
5. Privacy concerns: Some patients may be hesitant to share their personal information with digital therapeutics, due to concerns about data privacy and security.
6. Limited data: There is limited data on the long-term effects of digital therapeutics, so it is not yet clear how well they work over time.
7. Limited human oversight: Digital therapeutics may not provide the same level of human oversight as traditional therapies, which can be important for patients with complex medical conditions.

Artificial intelligence (AI) and machine learning (ML) technologies have the potential to be helpful in treating patients with sleep disorders in several ways:

• Predictive modeling: AI and ML can be used to analyze patient data and predict which patients are at the highest risk of developing sleep disorders. This can help healthcare providers to identify patients who may need additional monitoring or treatment.
• Diagnosis: AI and ML can be used to analyze patient data, including sleep study results, to help diagnose sleep disorders. Some AI algorithms have been developed to help identify sleep apnea, which is a common sleep disorder.
• Treatment recommendations: AI and ML can be used to analyze patient data and make personalized treatment recommendations. For example, AI algorithms can analyze patient data to identify the most effective treatment for insomnia.
• Monitoring: AI and ML can be used to monitor patients’ sleep patterns and provide alerts if there are any changes that may indicate a sleep disorder.
• Telemedicine: AI and ML can be used to analyze patient data remotely, which can be helpful in providing telemedicine services for patients with sleep disorders.

A digital twin is a virtual model of a physical system, process, or person, that can be used to simulate and analyze real-world conditions. In the context of treating patients with sleep disorders, a digital twin can be used to simulate a patient’s sleep patterns and analyze their sleep data. This can be helpful in several ways:

• Predictive modeling: A digital twin can be used to predict a patient’s risk of developing a sleep disorder and provide personalized treatment recommendations.
• Diagnosis: A digital twin can be used to analyze a patient’s sleep data, including sleep study results, to help diagnose sleep disorders.
• Treatment planning: A digital twin can be used to simulate different treatment options and predict their effectiveness for a particular patient.
• Monitoring: A digital twin can be used to monitor a patient’s sleep patterns and provide alerts if there are any changes that may indicate a sleep disorder.
• Personalized recommendations: A digital twin can be used to provide personalized recommendations for improving sleep based on a patient’s specific sleep patterns and data.
• Telemedicine: A digital twin can be used in telemedicine to provide remote consultations and follow-up appointments for patients with sleep disorders.

The digital twin initiative by Open Health Network and Miller School of Medicine aims to create a virtual model of a patient’s sleep patterns to help diagnose and treat sleep disorders. By simulating a patient’s sleep patterns and analyzing their sleep data, the digital twin can help identify specific issues and target them more effectively. This can lead to new treatment options for patients with sleep disorders.

The digital twin can be used to simulate different treatment options and predict their effectiveness for a particular patient. This can help healthcare providers to identify the most effective treatment for a patient’s sleep disorder, and make personalized treatment recommendations.

The digital twin can also be used to monitor a patient’s sleep patterns and provide alerts if there are any changes that may indicate a sleep disorder. This can help to ensure that patients with sleep disorders receive appropriate care and treatment in a timely manner.

Additionally, the digital twin can be used in telemedicine to provide remote consultations and follow-up appointments for patients with sleep disorders. This can be beneficial in providing access to treatment for patients who may have difficulty accessing in-person therapy and providing a convenient and cost-effective way for patients to manage their sleep disorders.

In conclusion, AI and digital twin technology have the potential to significantly improve the diagnosis, treatment, and management of sleep disorders. AI can be used to analyze large amounts of sleep data, predict the risk of developing sleep disorders, and make personalized treatment recommendations. Digital twin technology can be used to simulate access to patients’ sleep patterns and analyze their sleep data, which can help to identify specific issues and target them more effectively.

The integration of these technologies with an integrated care team approach can provide a comprehensive solution for patients with sleep disorders, improving the diagnosis, treatment, and management of sleep disorders, and ultimately improving the quality of life for patients.

The use of AI and digital twin technology has a great potential to provide more accurate, efficient, and personalized care for people suffering from sleep disorders, it can improve access to care and provide cost-effective solutions.

Sleep disorders: can AI and Digital Twin help? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.